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    <h1>Magic Table </h1>
    <em> by Dr Greg Ross</em>
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            <h1>Information visualisation</h1>
            <br><br>
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                    <h2 class="title">Table of contents</h2>
                    
                    <a name="toc"></a>  
                    <div id="toc"><ul><li><a href="#section1">1. Scientific visualisation</a></li><li><a href="#section2">2. Information visualisation</a></li><li><a href="#section3">3. Abstraction</a><ul><li><a href="#section3.1">3.1. Gestalt principles</a></li><li><a href="#section3.2">3.2. Visual structures</a></li><li><a href="#section3.3">3.3. Data types</a></li></ul></li><li><a href="#section4">4. Dimensionality</a><ul><li><a href="#section4.1">4.1. 1-dimensional visualisation</a></li><li><a href="#section4.2">4.2. 2-dimensional visualisation</a></li><li><a href="#section4.3">4.3. 3-dimensional visualisation</a></li><li><a href="#section4.4">4.4. 4+ -dimensional visualisation</a></li></ul></li><li><a href="#section5">5. Interactivity</a><ul><li><a href="#section5.1">5.1. Affordance and appropriation</a></li><li><a href="#section5.2">5.2. Time</a></li><li><a href="#section5.3">5.3. Interaction mechanisms</a><ul><li><a href="#section5.3.1">5.3.1. Overview plus detail</a></li><li><a href="#section5.3.2">5.3.2. Focus plus context</a></li></ul></li></ul></li><li><a href="#section6">6. Conclusions</a></li><li><a href="#section7">7. References</a></li></ul></div>
                    
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                <h2 class="title">What is InfoVis?</h2>
                <div id="mainContentText" class="entry">
                    
                    <p class="text">
                    Visualisation is of a holistic nature &ndash; it is more than the sum of its parts.  
                    It is essentially a cognitive aid that provides inspiration or insight into the 
                    previously latent relationships within data.  This is called cognitive amplification [<a href="#CMS99">CMS99</a>].  
                    This page begins by describing the field's roots in scientific visualisation before discussing 
                    some of the facets that comprise the visualisation of abstract data.
                    </p>
                    <br>
                    
                    <h2 id="section1">1. Scientific visualisation</h2>&nbsp;<a class="backTop" href="#toc">back to top</a><br><br>
                    
                    <p class="text">
                    In 1987 the National Science Foundation (NSF) in the United States published a report, 
                    Visualization in scientific computing [<a href="#MDB87">MDB87</a>] that paved the way for the fields of 
                    scientific and information visualisation.  The emphasis on visualisation was (and still is) 
                    dominant because the NSF recognised that visualisation provides scientists with a tool that 
                    can transform their myriad data into images that allow people to recognise patterns.  It 
                    was also realised that when visualising simulations of physical systems, the scientists 
                    could steer the simulations by changing the parameters used in their calculations and 
                    immediately gain visual feedback, whereas previously the calculations would require to be 
                    rerun in entirety.
                    </p>
                    
                    <p class="text">
                    Scientific visualisation occurs when physical data are represented by graphics portraying 
                    a physical system, allowing scientists to explore its properties.  In this way the 
                    visualisation is an external aid supporting the human's mental model of a system; it 
                    helps humans perceive its properties and amplifies cognition [<a href="#CMS99">CMS99</a>].  If it were not for 
                    apt visualisations then humans could easily fall foul of information overload.  If someone 
                    were to look at a database table consisting of thousands of records, each representing an 
                    object (datum), and each with numerous variables (columns), it would be almost impossible to 
                    gain an overview of structure in the data.  The virtue of visualisation also enhances 
                    communication and teaching because much of the information portrayed cannot be easily 
                    communicated in print [<a href="#DBM89">DBM89</a>].
                    </p><p>
                        
                    </p><p class="text">
                    Application areas for scientific visualisation include molecular modelling, medical imaging, 
                    meteorology, astrophysics, flow analysis and seismology. (see Figures 1 and 2).  The 
                    common property of the data predominant in such fields is that the variables are inherently 
                    spatial and can map directly on to a spatial substrate rendered on a screen.
                    </p>
                    
                    <p class="text">
                    A typical implementation for scientific visualisation systems is in the form of a modular 
                    data-flow architecture where data are piped through a set of modules, each of which has a 
                    specific purpose such as carrying out calculations, rendering or controlling parameter values.  
                    This piping of data is akin to the familiar UNIX pipe command for controlling the flow of data 
                    in inter-process communication [<a href="#Hae88">Hae88</a>].  The data-flow architecture allows the modules to be 
                    connected in a network that ultimately shapes the application with respect to its input, 
                    transformations and graphical rendering.  This notion has been extended to allow users to 
                    explicitly build the data-flow network, usually through direct manipulation of representations 
                    of the modules at the interface, and effectively build their own applications [<a href="#AT95a">AT95a</a>, <a href="#Bas00">BBB*93</a>, 
                    <a href="#Hae88">Hae88</a>, <a href="#UFK*89">UFK*89</a>].
                    </p>
                    
                    <div style="text-align:center;">
                        <img width="350" height="300" alt="" src="images/shuttle.bmp"><br>
                    </div>
                    <p class="figureCaption">
                    Figure 1:  A screenshot from the IBM DX scientific visualisation system [<a href="#AT95a">AT95a</a>] depicting
                    an unsteady flow simulation over a space shuttle launch vehicle.
                    </p>
                    <br><br><br>
                    
                    <div style="text-align:center;">
                        <img alt="" src="images/tsunami.png"><br>
                    </div>
                    <p class="figureCaption">
                    Figure 2:  Initial survey results obtained by HMS Scott showing images of the coastline of 
                    Sumatra where the earthquake that resulted in the Indian Ocean tsunamis occurred.  It is 
                    hoped that these visualisations will help scientists understand the cause of such natural 
                    phenomena and help predict them in the future.
                    </p>
                    <br><br><br>
                    
                    <h2 id="section2">2. Information visualisation</h2>&nbsp;<a class="backTop" href="#toc">back to top</a><br><br>
                    
                    <p class="text">
                    According to Card, Mackinlay and Shneiderman, the phrase Information Visualisation was first 
                    adopted in [<a href="#RCM89">RCM89</a>]. In this context information refers to non-physically based abstract data 
                    and visualisation is the use of computers to visually render these data in such a way that humans 
                    can interactively explore their structure.  Information visualisation is inspired by scientific 
                    visualisation but in this case the data to be turned into information are abstract and generally 
                    have no straightforward physical derivation. Figure 3 provides an example in which the abstract 
                    data, in this case search results returned by Google, can be interactively and pictorially 
                    summarised according to criteria such as hit rank and web-page size. 
                    </p>
                    
                    <div style="text-align:center;">
                        <img alt="" src="images/treemap.png"><br>
                    </div>
                    <p class="figureCaption">
                    Figure 3  A Honeycomb&copy; [<a href="#Hon04">Hon04</a>] view of results returned by the Google internet search engine. 
                    The visualisation is based upon Johnson and Shneiderman's treemap [<a href="#JS91">JS91</a>]: a technique designed 
                    to utilize space efficiently in the display of hierarchical information structures.
                    </p>
                    <br><br><br>
                    
                    <p class="text">
                    Information visualisations are holistic and have many facets such as interaction mechanisms, 
                    spatial representations and abstraction. The fields of Human Computer Interaction (HCI), 
                    cognitive, Gestalt and ecological psychology influence them.  The types of data and their 
                    volume in terms of data set size and dimensionality are key issues in determining their form, 
                    and therefore mathematical algorithms play a major part in both data transformations and 
                    visual rendering.
                    </p>
                    
                    <p class="text">
                    Applications of information visualisation include stock market analysis, project management, 
                    risk analysis, information retrieval etc. The literature presents many information visualisation 
                    techniques and workspaces borne of diverse architectures.  Some examples are Visage [<a href="#RCK*97">RCK*97</a>], 
                    IVEE [<a href="#AW95">AW95</a>], Information Visualizer [<a href="#CRM91">CRM91</a>] and snap-together visualisation [<a href="#NS00a">NS00a</a>, <a href="#NS00b">NS00b</a>, 
                    <a href="#Nor01">Nor01</a>, <a href="#NS01">NS01</a>]. Figure 4 shows an application of visualisation in project management. This tool 
                    is part of a commercial issues-tracking package [<a href="#Nic04">Nic04</a>] developed by the author for Nickleby HFE 
                    Ltd. In this case, the author has used the package to track issues relevant to his PhD research. 
                    The visualisation shown here is of a subset of the issues, arranged according to how they are 
                    interrelated.
                    </p>
                    
                    <div style="text-align:center;">
                        <img alt="" src="images/KIT Link map.png"><br>
                    </div>
                    <p class="figureCaption">
                    Figure 4:  The link map visualisation in nicklebyKIT&reg;. Each node represents an issue raised in 
                    respect to the author's research. Nodes that are deemed as being closely related are linked. The 
                    layout was produced by a force-directed placement algorithm for graph-drawing.
                    </p>
                    <br><br><br>
                    
                    <h2 id="section3">3. Abstraction</h2>&nbsp;<a class="backTop" href="#toc">back to top</a><br><br>
                    
                    <p class="text">
                    Abstraction is a conceptual representation of a physical (or non-physical) object.  In computer 
                    graphics this abstraction is the visual rendering of properties of the object.  This presents less 
                    of a problem in scientific visualisation because the properties tend to be physical.  However, in 
                    information visualisation, the properties of objects tend to have no straightforward derivation from 
                    physical space and thus the problem of creating visual representations that appeal to the human's 
                    perception is harder.
                    </p>
                    
                    <p class="text">
                    There are four prominent considerations in the process of creating visual representations of abstract 
                    data.  These are dimensionality, which will be discussed in more detail in the next section, data 
                    types, Gestalt principles and visual structures.  Each of these must be taken into account in order 
                    to provide an effective mapping of data onto a perceptual visual form, namely a 2- or 3- dimensional 
                    spatial substrate.
                    </p>
                    
                    <h3 id="section3.1">3.1. Gestalt principles</h3>&nbsp;<a class="backTop" href="#toc">back to top</a><br><br>
                    
                    <p class="text">
                    Humans can interpret visual information very quickly.  When we look at a picture, whether static or 
                    animated, our visual system allows us to perceive patterns and relationships between components of the 
                    picture.  For example, a group of points on a scatterplot, which are close together, will be perceived 
                    to be a cluster and therefore stand out as one perceptual unit.  This is illustrated in Figure 5.  
                    It is with regard to such automatic pattern or grouping detection that Gestalt principles exist.
                    </p>
                    
                    <p class="text">
                    Gestalt is the German translation of the word shape or form and is the inspiration for the Gestalt 
                    school of psychology that, in the early part of the 20th century, investigated some perceptual grouping 
                    properties and devised the Gestalt laws of grouping [<a href="#Rom01">Rom01</a>]. The table, below, provides a categorisation of these 
                    laws [<a href="#CMS99">CMS99</a>].
                    </p>
                    
                    <br><br>
                    
                    <div align="center">
                        <table cellspacing="0" cellpadding="5" bordercolor="#919191" border="1">
                            <thead>
                                <tr><th>Rule</th>
                                <th>Description</th>
                            </tr></thead>
                            <tbody>
                                <tr>
                                    <td>Prägnanz / Figural goodness</td>
                                    <td>Visual perception groups stimuli into a good figure. In this context, good means simple, regular, symmetrical etc.</td>
                                </tr>
                                <tr>
                                    <td>Familiarity</td>
                                    <td>Groups are more likely to appear if they seem familiar or meaningful.</td>
                                </tr>
                                <tr>
                                    <td>Similarity</td>
                                    <td>When presented with several stimuli, those that are similar to one another tend to be perceived as a group.</td>
                                </tr>
                                <tr>
                                    <td>Closure</td>
                                    <td>Contours that are spaced close together tend to be united.</td>
                                </tr>
                                <tr>
                                    <td>Good continuation</td>
                                    <td>A consecutive straight or curved path of close spacing through a set of objects is perceived as a group.</td>
                                </tr>
                                <tr>
                                    <td>Proximity</td>
                                    <td>Objects that are close to one another are perceived as a group / cluster.</td>
                                </tr>
                                <tr>
                                    <td>Common fate</td>
                                    <td>When objects are moving in the same direction they are seen as a group.</td>
                                </tr>
                            </tbody>
                        </table>
                    </div>
                    
                    <p class="figureCaption">
                    Table 1: Gestalt laws of grouping.
                    </p>
                    
                    <br><br><br>
                    
                    <p class="text">
                    The discovery of these principles means that they can be exploited to produce visualisations where 
                    the human can perceive aggregate structures or patterns to form a visual indexing.  This means 
                    that individual objects within a depiction become easier to find and thus in a good visualisation, 
                    exhaustive searching is not required.  For example, the spring model [<a href="#Ead84">Ead84</a>] was proposed to produce 
                    aesthetically pleasing graph layouts but has been widely used to produce layouts of general data objects. 
                    This meant that clusters could be formed and thus aid in analysing the intrinsic relationships within 
                    data [<a href="#Cha96">Cha96</a>]. 
                    </p>
                    
                    <div style="text-align:center;">
                        <img alt="" src="images/scatterplotCluster.png"><br>
                    </div>
                    <p class="figureCaption">
                    Figure 5:  A scatterplot has the potential to make groups of points appear as individual perceptual 
                    units (clusters). For example, the author would assume that in making reference to 'A' in the figure, 
                    the reader's attention would be drawn to the upper cluster as a whole and not the single point nearest 
                    to the label.
                    </p>
                    <br><br><br>
                    
                    <p class="text">
                    Also, in [<a href="#WAM01">WAM01</a>] time series data are mapped onto a spiral in order to make better use of screen real 
                    estate and to aid in the detection of cycles. This can be considered as an example of the good 
                    continuation rule and is illustrated in Figure 6.  However, it should be noted that the groups 
                    formed within a visual representation are only useful if they reflect actual relations within the 
                    data and are not a side-effect of the underlying rendering process.
                    </p>
                    
                    <div style="text-align:center;">
                        <img alt="" src="images/spiral.png"><br>
                    </div>
                    <p class="figureCaption">
                    Figure 6:  An example of the Gestalt principle of good continuation. Both of the above images represent 
                    sunshine intensity over an extended period of time, however, the spiral visualisation [<a href="#WAM01">WAM01</a>] more 
                    clearly shows the day/night periods.
                    </p>
                    <br><br><br>
                    
                    <p class="text">
                    The Gestalt principles of organisation indicate that in a good layout, abstract data can be organised 
                    to provide a visualisation that reveals information in the structure and relationships within the data.  
                    As a final example on the importance of the Gestalt principles, consider the following figure:
                    </p>
                    
                    <div style="text-align:center;">
                        <img alt="" src="images/stickman.bmp"><br>
                    </div>
                    <p class="figureCaption">
                    Figure 7: The familiarity rule.
                    </p>
                    <br><br><br>
                    
                    <p class="text">
                    The above figure illustrates that at the heart of Gestalt theory is the proposition that in perception 
                    the whole is more than the sum of its parts.
                    </p>
                    
                    <h3 id="section3.2">3.2. Visual structures</h3>&nbsp;<a class="backTop" href="#toc">back to top</a><br><br>
                    
                    <p class="text">
                    The mapping of data as abstract objects to symbols within a space is closely related to the Gestalt 
                    principles, specifically the similarity rule described above. However, there is more to visualisation 
                    than merely grouping similar objects.  In the case of using visualisations for analysis and problem 
                    solving, individual entities may require comparison.  For example, in a frequency domain graph, at 
                    what frequencies are the highest magnitudes exhibited?  This brings to bear the need to distinguish 
                    between the types of variable considered and the spatial substrate in which they are represented. 
                    The main categories for variable types are as follows:
                    </p>
                    
                    <br><br>
                    
                    <div align="center">
                        <table cellspacing="0" cellpadding="5" bordercolor="#919191" border="1">
                            <thead>
                                <tr><th>Category</th>
                                <th>Description</th>
                            </tr></thead>
                            <tbody>
                                <tr>
                                    <td style="font-style:italic;">Nominal</td>
                                    <td>can only be = or != to other values</td>
                                </tr>
                                <tr>
                                    <td style="font-style:italic;">Ordinal</td>
                                    <td>can obey <, ≤, > and ≥ relations</td>
                                </tr>
                                <tr>
                                    <td style="font-style:italic;">Quantitative</td>
                                    <td>continuous values allowing mathematical axioms of division, multiplication, subtraction and addition</td>
                                </tr>
                            </tbody>
                        </table>
                    </div>
                    
                    <p class="figureCaption">
                    Table 2: Variable types.
                    </p>
                    
                    <br><br><br>
                    
                    <p class="text">
                    In [<a href="#CM97">CM97</a>], a list of graphical properties is described and the appropriate mapping of variable types to some 
                    of these is demonstrated.  The graphical properties are marks (including points, lines, areas, surfaces and 
                    volumes), position in space, and retinal properties, including shape, size, orientation etc.  It has been 
                    established that certain variable types are better mapped onto specific graphical properties than others, 
                    i.e. some properties are more effective encoders of information than others.  For example, in [<a href="#CMS99">CMS99</a>] it 
                    is stated that greyscale is better for encoding and comparing nominal variables than quantitative variables.
                    </p>
                    
                    <p class="text">
                    The careful use of graphical properties is essential in creating a visualisation that communicates 
                    information to the user.  There have been a number of models proposed which aim to classify data by 
                    the type of visualisation that could best convey information.  Several of these are described in [<a href="#Rob99">Rob99</a>] 
                    where an algebraic method is proposed to describe visualisations in order to guide the visual designer in 
                    creating the most effective depiction of abstract data.
                    </p>
                    
                    <h3 id="section3.3">3.3. Data types</h3>&nbsp;<a class="backTop" href="#toc">back to top</a><br><br>
                    
                    <p class="text">
                    Where the type of variables considered in a visualisation can suggest the most appropriate representative 
                    glyphs and symbols, the overall intrinsic structure of data (internal relationships and dimensionality) can 
                    suggest the utilisation of pre-existing visualisation types.  As an example, temporal data may lend itself 
                    to being presented as a Gantt chart.
                    </p>
                    
                    <p class="text">
                    In [<a href="#Shn96">Shn96</a>], Shneiderman describes a Task by Data Type Taxonomy whereby a designer can choose between given 
                    examples of visualisations depending upon the type of data to be processed.  The seven data types Shneiderman 
                    outlines are:
                    </p>
                    
                    <ul>
                        <li>1-dimensional</li>
                        <li>2-dimensional</li>
                        <li>3-dimensional</li>
                        <li>multi-dimensional</li>
                        <li>temporal</li>
                        <li>tree</li>
                        <li>network</li>
                    </ul>
                    
                    <br>
                    <br>
                    
                    <p class="text">
                    This taxonomy was devised with Shneiderman's Visual Information seeking mantra 
                    [<a href="#Shn96">Shn96</a>] in mind: "Overview first, zoom and filter, then details-on-demand."  Shneiderman suggested 
                    that each component of the mantra is one of the salient tasks in visual information seeking.
                    </p>
                    
                    <p class="text">
                    The major point of this section is to show that when a data type is known, there may already be 
                    tried and tested techniques for presenting a visualisation and therefore provide a basis for discussion 
                    or prevent the designer from 're-inventing the wheel' for new tools.
                    </p>
                    
                    <br><br>
                    
                    <h2 id="section4">4. Dimensionality</h2>&nbsp;<a class="backTop" href="#toc">back to top</a><br><br>
                    
                    <p class="text">
                    Dimensionality pertains to the number of attributes or variables that are to be considered for every object 
                    within a visualisation.  For example, a geographical position can be described by two variables: latitude 
                    and longitude. Dimensionality is an important consideration in information visualisation because humans 
                    can only readily perceive structures within a low number of dimensions.  If a set of objects of three 
                    dimensions or less is to be visualised, then the dimensions can be mapped directly onto a set of orthogonal 
                    axes. Considering the example above, a set of geographical positions may be displayed by mapping longitude 
                    to the x-axis and latitude to the y-axis, while maintaining the proportional distance interrelationships 
                    between points.  However, there are many cases where the entities to be visualised have many dimensions and 
                    therefore there is no direct mapping to a 2- or 3-dimensional substrate.  As an example, consider the 
                    visualisation of a corpus of textual documents where each unique word contained within the set is regarded 
                    as a dimension.  In this case the dimensionality of the space in which the objects reside can go into the 
                    tens of thousands.
                    </p>
                    
                    <p class="text">
                    In this section some of the techniques that have been applied to the visualisation of low (≤3) 
                    and high (&gt;3) dimensional data will be discussed.
                    </p>
                    
                    <br><br>
                    
                    <h3 id="section4.1">4.1. 1-dimensional visualisation</h3>&nbsp;<a class="backTop" href="#toc">back to top</a><br><br>
                    
                    <p class="text">
                    A common example of 1-dimensional data is a list.  Lists may be composed of any variable types, but in this 
                    section strings represented as ordinals will be considered.  In [<a href="#Eic94a">Eic94a</a>] a visualisation tool called SeeSoft 
                    is presented where lines of source code are greatly visually compressed into narrow rectangles along a 
                    folding axis (see Figure 8).  The author of [<a href="#Eic94a">Eic94a</a>] describes this method as reduced representation and 
                    claims that up to 50,000 lines of code can be displayed within one screen.  The beauty of this approach is 
                    that the reduced representation holds all of the spatial pattern information within the data set in the 
                    same way as the original text, but reduced in size so that an overview is gained that maintains recognisable 
                    groupings of the unreduced text.  The system also offers interactive features such as a magic lens to allow 
                    users to magnify and read sections of code.  The retinal variable, colour, is also used to map statistical 
                    information such as modification requests to lines of code.  In this way, the user can scan the overview of 
                    the code and automatically process the colour information to detect patterns and areas of interest for deeper 
                    examination.  It may be argued that one of the contributors to the effectiveness of this visualisation is the 
                    familiarity rule of the Gestalt principles.  The reduced representation of the source code does not distort 
                    the proportional natural layout of the data and therefore sections (groups) of lines may remain recognisable. 
                    The SeeSoft tool is also a good example of a visualisation of 1-dimensional data because it exhibits the use 
                    of a folding axis.  A folding axis is an axis that is designed to use available space more efficiently by 
                    folding back on its self at certain points (again, see Figure 8).  It is a 2-d method for visualising 1-d 
                    data and therefore can be considered as a 'dimension expansion' technique. This technique can be applied to 
                    the visualisation of data of dimensionality d &gt; 1, but it is most effective when variables of only one 
                    dimension are to be depicted.
                    </p>
                    
                    <div style="text-align:center;">
                        <img alt="" src="images/folding axis.png"><br>
                    </div>
                    <p class="figureCaption">
                    Figure 8: An example of reduced representation. The figure depicts two source code modules, each of which is on a folding axis.
                    </p>
                    <br><br><br>
                    
                    <h3 id="section4.2">4.2. 2-dimensional visualisation</h3>&nbsp;<a class="backTop" href="#toc">back to top</a><br><br>
                    
                    <p class="text">
                    When dealing with data of two dimensions, the visualisation process is often based upon a 
                    simple direct mapping onto two axes.  The most common form of a 2-dimensional visualisation is a geographical 
                    map where locations are placed according to the longitude and latitude variables.
                    </p>
                    
                    <p class="text">
                    Data that are comprised of two-variable entities are described as planar.  This is because they map directly 
                    onto a flat 2-dimensional surface or plane.  However, an interesting twist in the display of a 2-dimensional 
                    layout was proposed in [<a href="#MRC91">MRC91</a>] where a 'perspective wall', shown in Figure 9, is described to transform 2-d 
                    layouts into a 3-d representation. The basic idea is that 2-d layouts with large aspect ratios can be distorted 
                    so that the central part of the layout is entirely visible to the user while the far left and right portions 
                    appear to stretch off into the distance. This is a technique inspired by the bifocal lens [<a href="#SA82">SA82</a>, <a href="#ATS82">ATS82</a>]. This 
                    serves the purpose of affording the user detail and overview simultaneously and is closely related to the 
                    ideas of Furnas [<a href="#Fur86">Fur86</a>]. The perspective wall is also an example of a type of folding axis in the 2-d case, 
                    where the two dimensions of the plane are folded in the direction of the third dimension (away from the user).
                    </p>
                    
                    <div style="text-align:center;">
                        <img alt="" src="images/perspective_wall.bmp"><br>
                    </div>
                    <p class="figureCaption">
                    Figure 9: The 'perspective wall' distorts a 2-d layout so that the focus at the centre of the screen is most 
                    legible while the remainder of the layout is peripheral. The user can scroll potentially interesting parts of 
                    the layout to the fore and still be afforded the context of neighbouring regions.
                    </p>
                    <br><br><br>
                    
                    <h3 id="section4.3">4.3. 3-dimensional visualisation</h3>&nbsp;<a class="backTop" href="#toc">back to top</a><br><br>
                    
                    <p class="text">
                    3-dimensional visualisation is most prominent in the field of scientific visualisation where collective 
                    bodies of 3-d physical data are often the basis of analysis.  When a physical object is modelled it is useful 
                    to render it in three dimensions to conform to the mental model of the person who is viewing it.  Examples 
                    include the visualisation of molecular structures and the physiology of the human body.
                    </p>
                    
                    <p class="text">
                    Although 3-dimensional abstract data can be directly mapped into a 3-dimensional visualisation space, for 
                    example a 3-d bar chart could be used to depict a company's profit for different products across various cities, 
                    abstract data often gains little from this embellishment.  This may be because there is no inherent physical 
                    mental model to sustain. On the other hand, there have been attempts to use a 3-dimensional space to navigate 
                    complex data structures.  In [<a href="#HK97">HK97</a>], a system called Cat-a-Cone consists of a hierarchical ConeTree [<a href="#RMC91">RMC91</a>] 
                    which is displayed in three dimensions to make better use of screen real estate (see Figure 10). In [<a href="#Ren94">Ren94</a>] 
                    a tool named Galaxy of News organises textual information in a 3-d space where similarity between texts is 
                    reflected by their proximity to one another. As the user navigates through the space semantic zooming is employed 
                    to show or elide text and detail, depending upon the user's position in the space.  However, there can be serious 
                    disadvantages to rendering abstract data in three dimensions.  A problem exhibited by the Cat-a-Cone system is 
                    that the nodes of the tree can become occluded and therefore the amount of information to be gleaned at any one 
                    time is reduced.  Also, in the Galaxy of News system, the lack of a referential horizon and ground plane can 
                    cause the user to be disoriented.  In the words of Chalmers [<a href="#Cha93">Cha93</a>], "Our skills in...mental model-making, as honed 
                    on our everyday '2.1D' world, become more difficult to employ."  In the context of this quote, Chalmers describes 
                    a metaphor of a 2.1-d landscape for representing the distribution of a corpus of documents.  This type of 
                    visualisation can be called an information landscape or themescape [<a href="#WTP*95">WTP*95</a>] and is based upon the premise that 
                    the metaphor can provide landmarks and other natural aids to allow the user to build a mental map of the corpus. 
                    Figure 11 depicts a visualisation based upon Wise's themescape [<a href="#WTP*95">WTP*95</a>].
                    </p>
                    
                    <div style="text-align:center;">
                        <img alt="" src="images/cat-a-con.bmp"><br>
                    </div>
                    <p class="figureCaption">
                    Figure 10: Cat-a-Cone [<a href="#HK97">HK97</a>] arranges each level of a hierarchical categorisation scheme in a 3-d view to 
                    utilise space efficiently.  This technique, like the 'perspective wall', uses perspective distortion to clarify 
                    the focus (the node closest to the viewer) while maintaining the context of the adjacent nodes.
                    </p>
                    <br><br><br>
                    
                    <p class="text">
                    Although 3-dimensional visualisations can be impressive, they do, in general, create cumbersome overheads.  
                    They require more powerful hardware and require more intensive processing in the visual transformations; 
                    navigation is more complex because at least six degrees of freedom of movement may be required and it is 
                    more difficult to incorporate textual objects that are often predominant in information visualisation [<a href="#CMS99">CMS99</a>].
                    </p>
                    
                    <div style="text-align:center;">
                        <img alt="" src="images/spire.bmp"><br>
                    </div>
                    <p class="figureCaption">
                    Figure 11: A screenshot from Spire [<a href="#Wis99">Wis99</a>] &ndash; a tool based upon Wise's themescape [<a href="#WTP*95">WTP*95</a>]. A document corpus 
                    is represented via a landscape metaphor in which the themes that run through the collection are mapped to 
                    visual attributes. 
                    </p>
                    <br><br><br>
                    
                    <h3 id="section4.4">4.4. 4+ -dimensional visualisation</h3>&nbsp;<a class="backTop" href="#toc">back to top</a><br><br>
                    
                    <p class="text">
                    In statistical analysis, data sets that are comprised of objects consisting of more than three variables are 
                    described as multivariate or hypervariate and are considered as n-attribute items dispersed within an n-dimensional 
                    space. Thus, in information visualisation these generally come under the rubric of the multidimensional. There has 
                    been a great deal of work concentrating on the visualisation of multidimensional data, spurred on by the fact that 
                    there is no possible way of directly mapping multidimensional objects onto a set of visually perceptive axes.  
                    However, there are some shortcuts available to multidimensional data at the lower end of the scale.  For example, 
                    three dimensions of 4-dimensional data may be mapped onto points in a 3-d substrate and the fourth dimension mapped 
                    to colour, or shape, but beyond this, more innovative techniques must be derived.
                    </p>
                    
                    <p class="text">
                    Spoerri [<a href="#Spo93">Spo93</a>] proposes a tool called InfoCrystal (Figure 12) for querying and visualising results for 
                    information retrieval. The idea is to generalise the Venn diagram to discretely display the distribution of objects 
                    of more than three dimensions. This system is intuitive because of the familiarity with the Venn diagram; however, 
                    as the number of dimensions to be depicted increases the complexity of the graphics soon becomes overwhelming.
                    </p>
                    
                    <div style="text-align:center;">
                        <img alt="" src="images/InfoCrystal.png"><br>
                    </div>
                    <p class="figureCaption">
                    Figure 12: An InfoCrystal [<a href="#Spo93">Spo93</a>] representing three search criteria or inputs, A, B and C and all possible Boolean 
                    queries in normal conjunctive form. The interior icons can be embellished to show the results of submitting the 
                    respective queries to a document collection. In this example, these inputs define a 3-d search space, however, 
                    Spoerri has demonstrated the application of InfoCrystals to more than three inputs.
                    </p>
                    <br><br><br>
                    
                    <p class="text">
                    Tweedie et al. [<a href="#TSDS96">TSDS96</a>] present the 'Prosection Matrix'. This idea stems from the statistical technique of 
                    representing all possible combinations of pairs of variables for a data set as a matrix of scatterplots.  
                    They embellished this technique by adding an interaction technique called brushing [<a href="#BC87">BC87</a>] that allows selected 
                    points in one scatterplot to be highlighted in others. In this case the brushing entails using sliders (one for 
                    each dimension/parameter) to define selected parameter ranges so that points in one scatterplot, depicting the 
                    relationship between p1 and p2 for instance, can be highlighted according to the selected range of p3 for example.  
                    Hence the name prosection was derived from projection of a section. This technique, like that in InfoCrystal, also 
                    becomes intractable for visualising data of many dimensions because the number of scatterplots required is equal 
                    to N(N &ndash; 1)/2 where N is the number of dimensions.
                    </p>
                    
                    <p class="text">
                    As the dimensionality of data increases, the plausible techniques for clearly depicting the influence of all of 
                    the attributes falls sharply in number and in effectiveness.  It is partly for this reason that methods such as 
                    Multidimensional Scaling (MDS), Principal Components Analysis and a plethora of clustering algorithms exist. 
                    Specifically, in information visualisation, their role is to map the objects from their high-dimensional space to 
                    points in two or three dimensions.
                    </p>
                    
                    <p class="text">
                    Lin et al. [<a href="#LRP95">LSM91</a>] take advantage of Kohonen's self-organising feature map (SOM) [<a href="#KKL*00">KKL*00</a>], to map high-dimensional 
                    textual documents onto a discrete 2-d grid. As stated earlier, a corpus of textual documents has dimensionality 
                    roughly equal to the number of unique terms contained within, and therefore it is impossible to directly map the 
                    documents as points in this high-dimensional space into two or three dimensions.  Lin et al. proposed that the SOM 
                    could be used to create concept areas in the plane of the SOM which would effectively partition the corpus into 
                    classes and thus give insight into the topology of the corpus at a glance (see Figure 13).
                    </p>
                    
                    <div style="text-align:center;">
                        <img alt="" src="images/SOM 1.bmp"><br>
                    </div>
                    <p class="figureCaption">
                    Figure 13: An example of the output of a SOM, depicting the concept areas relating to electronics.
                    </p>
                    <br><br><br>
                    
                    <p class="text">
                    The only drawback with this approach is that only the topology of the corpus is communicated.  Relationships 
                    between individual documents cannot be visualised as only the cluster centres are depicted and the discrete 
                    grid-like output of the SOM ensures that these are all evenly spaced.
                    </p>
                    
                    <p class="text">
                    In a paper by Rodden et al. [<a href="#RBSW01">RBSW01</a>], another discrete visualisation maps images onto a grid to aid in browsing.  
                    In this case an unspecified MDS algorithm is used to create a continuous 2-d layout of objects so that similar images 
                    are placed close together, and then one of several algorithms proposed by Basalj [<a href="#Bas00">Bas00</a>] is utilised to discretise 
                    the space in order to remove occlusions. This approach may be considered as an alternative heuristic to the SOM.
                    </p>
                    
                    <br><br>
                    
                    <h2 id="section5">5. Interactivity</h2>&nbsp;<a class="backTop" href="#toc">back to top</a><br><br>
                    
                    <p class="text">
                    It is difficult to communicate the intrinsic and latent relationships within high-dimensional abstract data through 
                    a single static representation.  For this reason, mechanisms which afford the user interactive control over the 
                    representation are required to unlock the information that can only be revealed in dynamic visualisations.
                    </p>
                    
                    <h3 id="section5.1">5.1. Affordance and appropriation</h3>&nbsp;<a class="backTop" href="#toc">back to top</a><br><br>
                    
                    <p class="text">
                    An important aspect that blends interactive visualisation with the premise of good graphical user interface (GUI) 
                    design is the issue of affordance.  Affordance may be defined as the ability for an object to be perceived by the 
                    user as usable according to his or her physical and mental abilities.  The user quickly understands the use of a 
                    device for a given function or activity.  When affordance exists in the design of an interface, whether it is 
                    physical or in the digital domain, the resulting system is easier and maybe even pleasant to use [<a href="#Nor88">Nor88</a>].  
                    Sometimes affordances can be accidental, in which case the user may appropriate the functions to his or her own 
                    ends in different ways to those the interface designer intended or even considered.  From the perspective of 
                    interface evaluation via observation of use, this can be advantageous in offering insight into the correct way 
                    to implement complex functions.
                    </p>
                    
                    <h3 id="section5.2">5.2. Time</h3>&nbsp;<a class="backTop" href="#toc">back to top</a><br><br>
                    
                    <p class="text">
                    Another important consideration in the design of interactive systems is the speed of interaction.  In [<a href="#CRM91">CRM91</a>] 
                    three categories of interaction speed are described:
                    </p>
                    
                    <br><br>
                    
                    <div align="center">
                        <table cellspacing="0" cellpadding="5" bordercolor="#919191" border="1">
                            <thead>
                                <tr><th>Time</th>
                                <th>Category</th>
                                <th>Description</th>
                            </tr></thead>
                            <tbody>
                                <tr>
                                    <td>0.1 seconds</td>
                                    <td>perceptual processing</td>
                                    <td>Stimuli presented within 0.1s of each other are perceived to be a single stimulus.  
                                    An example of this is in animations comprised of several stills.</td>
                                </tr>
                                <tr>
                                    <td>1 second</td>
                                    <td>immediate response</td>
                                    <td>The minimum time in which a user may respond to stimuli.</td>
                                </tr>
                                <tr>
                                    <td>10 seconds</td>
                                    <td>unit task</td>
                                    <td>Described as the time taken for a simple action, requiring minimal cognition.</td>
                                </tr>
                            </tbody>
                        </table>
                    </div>
                    
                    <p class="figureCaption">
                    Table 3: Categories of interaction speeds.
                    </p>
                    
                    <br><br><br>
                    
                    <p class="text">
                    Inspired by the interaction between humans, Robertson et al. [<a href="#RCM89">RCM89</a>], proposed an interface architecture called 
                    the cognitive coprocessor to match the impedance between the user and an automated information agent. Essentially, 
                    the response times of the system should match the capabilities and expectations of the user when reacting to 
                    stimuli and carrying out elemental tasks.
                    </p>
                    
                    <p class="text">
                    Shneiderman [<a href="#Shn83">Shn83</a>] describes Direct Manipulation, which shows that a short response time for visual 
                    feedback is very important. Direct manipulation can be described as a metaphor for manipulating 
                    graphical objects as if using one's own hands, in order to conform to the user's expectations of what 
                    should happen.  For example, when a file is dragged over the recycle bin on a Windows OS desktop, and 
                    then let go, the file disappears as if the file has fallen into the bin.     Shneiderman also describes 
                    the supplanting of textual query languages (in the user interface) such as SQL with direct manipulation 
                    in the form of Dynamic Queries [<a href="#Shn94">Shn94</a>].  Dynamic queries provide immediate feedback during query formulation 
                    by updating results as the queries are built.
                    </p>
                    
                    <p class="text">
                    From the above, it can be considered that direct manipulation mechanisms must react to the user's actions 
                    within 0.1 seconds for the perceived continuity of physical motion.
                    </p>
                    
                    <h3 id="section5.3">5.3. Interaction mechanisms</h3>&nbsp;<a class="backTop" href="#toc">back to top</a><br><br>
                    
                    <p class="text">
                    In a paper by Shneiderman [<a href="#Shn96">Shn96</a>], his visual information seeking mantra is described: "Overview first, 
                    zoom and filter, then details-on-demand". According to Shneiderman this indicates the basic elements required 
                    in an interactive visualisation when seeking information.  However, this implies that visual information 
                    seeking is a sequential process where a series of views are presented in isolation. The following sub-sections 
                    describe interaction mechanisms that have been developed to integrate such views so that overview and 
                    detail can be presented simultaneously, and zooming and filtering can be applied within the context of the 
                    original view.
                    </p>
                    
                    <h4 id="section5.3.1">5.3.1. Overview plus detail</h4>
                    
                    <p class="text">
                    An overview of a visual representation is important to afford the user navigation and pattern detection.  
                    As a result, searching can be enhanced.  However, both the whole overview and the finer-grained details of 
                    local data structures are often required to facilitate analysis and evaluation of smaller portions of data.  
                    The overview enables a high-level view to help orient the user while (s)he drills down into the details.
                    </p>
                    
                    <p class="text">
                    A typical guise of overview plus detail is the zoom function.  In Eick's SeeSoft tool [<a href="#Eic94a">Eic94a</a>], a separate 
                    window can be shown over the reduced representation in order to allow the user to read individual lines of code. 
                    The advantage of this is that the user may perceive where he or she is within the overview and also gain finer details of that 
                    area.  This, applied in SeeSoft, is an example of focus plus context and mimics the human's visual system where the 
                    bandwidth is split between the peripheral view and the higher resolution focus [<a href="#CMS99">CMS99</a>]. This allows people to understand 
                    something by its context as well as its detail within the context.
                    </p>
                    
                    <p class="text">
                    Two everyday examples of overview + detail include Windows Explorer &ndash; the overview is provided by a treeview 
                    while the detail is shown as a set of file and folder icons in a separate pane &ndash; and Adobe Acrobat [<a href="#Ado04">Ado04</a>] where 
                    the thumbnail view gives a (reduced representation) overview of a PDF file, next to the detailed text.  North, 
                    Shneiderman and Plaisant [<a href="#NSP96">NSP96</a>] contributed a more novel application in the visualisation of a medical digital library. 
                    The overview is a longitudinal cut of a human body and the detail view consists of an axial cross-section (see Figure 14).
                    </p>
                    
                    <div style="text-align:center;">
                        <img alt="" src="images/OverviewDetail.bmp"><br>
                    </div>
                    <p class="figureCaption">
                    Figure 14: North et al.'s Visible Human Explorer interface. The overview of the human body is tightly coupled 
                    with axial detail view. The user can sweep a horizontal line across the overview to dynamically update the 
                    detailed cross-section view.
                    </p>
                    <br><br><br>
                    
                    <p class="text">
                    The following subsection is inspired by the problem of overview plus detail and describes some of the methods for achieving it.
                    </p>
                    
                    <h4 id="section5.3.2">5.3.2. Focus plus context</h4>
                    
                    <p class="text">
                    Focus plus context is related to overview plus detail by the fact that the context is provided by the overview and 
                    the focus is on the finer detail.  A classic example of the focus plus context method is Furnas's Generalised 
                    Fisheye Views [<a href="#Fur86">Fur86</a>].  In this paper, Furnas defines a degree of interest (DOI) function that is used to assign 
                    a number reflecting the importance to the user of an object within a visual structure, given his or her current 
                    task.  This number can then be used to reduce or remove detail from less important areas of the view.
                    </p>
                    
                    <p class="text">
                    This is an example of a distortion technique similar to the Perspective Wall [<a href="#MRC91">MRC91</a>].  Another example of 
                    view distortion for focus plus context is the hyperbolic tree [<a href="#LRP95">LRP95</a>], where it is proposed that by mapping 
                    large hierarchical trees onto a hyperbolic plane, the hyperbolic geometry will create a fisheye-like distortion.  
                    The part of the plane that is the least distorted (more detailed and larger) is that which is closest to the 
                    viewpoint in the centre of the screen, i.e. the focus, whereas other areas are more distorted and shrink the 
                    embedded objects as the distance from the focus increases. Direct manipulation is used here to rotate the 
                    hyperbolic plane and thus move the focus, and as a result very large tree hierarchies may be displayed.
                    </p>
                    
                    <p class="text">
                    Zooming is another well established technique for gaining insight into detailed areas of a view while 
                    maintaining the context.  There are two predominant types of zoom mechanisms: semantic and logical.  
                    Logical zooming lends itself more to our familiar notion of zooming as it describes the perspective 
                    notion of objects being larger when closer and smaller as the distance between them and the viewer 
                    increases. For this reason, logical zooming can be described as physical or geometric because our 
                    psychophysical perspective pertains mainly to changes in object size and colour saturation for this 
                    type of zooming. The SeeSoft tool described above makes use of logical zooming.
                    </p>
                    
                    <p class="text">
                    On the other hand, semantic zooming, which may or may not contain aspects of logical zooming, pertains 
                    more to the idea that as the area of focus approaches objects, the level of abstraction is changed &ndash; 
                    mappings of data attributes to graphical properties change.  A user-interface proposed by Bederson and 
                    Hollan called Pad++ [<a href="#BH94">BH94</a>] provides semantic zooming.  Here, direct manipulation of a focus point is 
                    used to provide additional details to objects that appear under the focus.  The Magic Lens [<a href="#FS95">FS95</a>] is 
                    another example of the zooming paradigm and has been demonstrated as a way to supplant textual database 
                    querying because multiple lenses (focus points) can be used in conjunction to form Boolean expressions 
                    that filter or abstract details of the objects being visualised.
                    </p>
                    
                    <p class="text">
                    This now leads on to describing filtering within an information space.  As stated earlier it is difficult 
                    to map high dimensional abstract data into a single static view.  As a result MDS techniques have been 
                    devised to reduce the dimensionality in order to be able to display high-dimensional objects in a 2- or 
                    3-d point space, usually preserving some distance function.  Although this provides an overview of the 
                    data set's distribution, the contribution of individual attributes can be hard to interpret in these 
                    scatterplot-like displays &ndash; some variables may be more dominant than others.  It is for this reason 
                    that filtering comes into its own as a means for drilling down into the data to help find latent 
                    relationships.  As described, the magic lens is one means but there are many other GUI components 
                    that may afford the filtering process.  One such component is the slider control, which provides an 
                    example of direct manipulation for view transformation.  In [<a href="#CM97">CM97</a>] it is stated that using sliders, 
                    a user can take into account additional variables without these being mapped to retinal properties.
                    </p>
                    
                    <p class="text">
                    In essence, a slider is a GUI control that allows the user to define ranges (double-ended sliders) or to 
                    select individual values or thresholds.  Eick [<a href="#Eic94b">Eic94b</a>] describes the use of sliders to filter or highlight 
                    items within a view as determined by the value or range of values selected by the slider.  An example of 
                    which can be found in Ahlberg and Shneiderman's filmfinder [<a href="#AS94a">AS94a</a>] as shown in Figure 15.  In filmfinder, 
                    the range selected in a double-ended slider maps to a zoom function, while the position of the range, along 
                    the slider's scale, maps to a pan function.
                    </p>
                    
                    <p class="text">
                    Eick then goes on to describe graphical embellishments such as using the space inside the slider to 
                    depict the distribution of the data being analysed (see Figure 16) and thereby provide clues in 
                    information seeking.
                    </p>
                    
                    <div style="text-align:center;">
                        <img alt="" src="images/filmfinder.png"><br>
                    </div>
                    <p class="figureCaption">
                    Figure 15:  Double-ended sliders to the left and bottom of the plot in filmfinder allow the user 
                    to zoom and pan along the two axes, essentially filtering the view.
                    </p>
                    <br><br><br>
                    
                    <div style="text-align:center;">
                        <img alt="" src="images/double-slider bmp.bmp"><br>
                    </div>
                    <p class="figureCaption">
                    Figure 16:  A double-ended slider with a histogram, showing a range selection.
                    </p>
                    <br><br><br>
                    
                    <p class="text">
                    Tweedie et al. [<a href="#TSDS96">TSDS96</a>] also make use of this type of enhanced slider.  In their influence 
                    explorer tool, where histograms are used in conjunction with sliders, the sliders are all 
                    interlinked so that when the selection of one slider is changed, the effect can be seen by 
                    highlighting sections of the histograms of other sliders.
                    </p>
                    
                    <br><br>
                    
                    <h2 id="section6">6. Conclusions</h2>&nbsp;<a class="backTop" href="#toc">back to top</a><br><br>
                    
                    <p class="text">
                    In this page some of the techniques used in information visualisation, and their motivations, 
                    have been described. Information visualisation serves as a key to unlock the black box of abstract 
                    data, to reveal the interrelationships and salient properties. It is holistic in nature &ndash; it is 
                    more than just a collection of glyphs, axes and graphical structures. Through abstraction, 
                    visualisation can help the user create mental models of the data and gain insight into their 
                    structure. Through interaction, the user is prompted to ask questions and then be provided 
                    with the answers.  It can afford the user navigation and browsing of abstract data whose 
                    elements reside in a bewildering number of dimensions.   It is envisaged that in the future 
                    many more interesting and novel devices will be devised to aid the user's perception of 
                    complex data and their interrelationships.
                    </p>
                    
                    <p class="text">
                    Visualisation relies upon intuitive reduction of data so that their representation is simplified 
                    and therefore information is easier to convey. A very popular means of attaining this is via cluster 
                    analysis. By considering data as points in a data space, clustering algorithms can find contiguous 
                    groups of closely related points thus reducing the representative size of the data set to the number 
                    of clusters. Another approach is to reduce the dimensionality of the data so that it conveys as 
                    much of the original information as possible using a small number of derived dimensions. 
                    Clustering and dimension reduction algorithms often go hand-in-hand to 
                    create hybrid solutions that provide an efficient and effective basis for visualisation.
                    </p>
                    
                    <br><br>
                    
                    <h2 id="section7">7. References</h2>&nbsp;<a class="backTop" href="#toc">back to top</a><br><br>
                    
                    <div align="left">
                        <table cellspacing="0" cellpadding="5" bordercolor="#1E1E1E" border="1">
                            <tbody>
                                <tr>
                                    <td valign="top">[<a name="Ado04">Ado04</a>]</td>
                                    <td>Adobe Acrobat Reader 5.0. http://www.adobe.com/acrobat (2004).</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="AS94a">AS94a</a>]</td>
                                    <td>Ahlberg C., Shneiderman B.: Visual Information Seeking using the FilmFinder. 
                                    <span class="it">Proceedings of ACM CHI'94 Conference on Human Factors in Computing Systems 2,</span>
                                    (1994), 433.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="AT95a">AT95a</a>]</td>
                                    <td>Abram G., Treinish L.: An Extended Data-Flow Architecture for Data Analysis and Visualization. 
                                    <span class="it">Computer Graphics 29,</span> 3 (May 1995). 17&ndash;21.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="ATS82">ATS82</a>]</td>
                                    <td>Apperley M. D., Tzavaras I., Spence R.: A Bifocal Display Technique for Data Presentation. 
                                    <span class="it">Proceedings of Eurographics'82, Conference of the European Association for Computer Graphics</span>
                                     (1982), 27&ndash;43.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="AW95">AW95</a>]</td>
                                    <td>Ahlberg C., Wistrand E.: IVEE: An Information Visualization and Exploration Environment. 
                                    <span class="it">Proceedings of InfoVis '95, IEEE Symposium on Information Visualization </span>
                                    (October 1995), 66&ndash;73.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="Bas00">Bas00</a>]</td>
                                    <td>Basalaj W.: <span class="it">Proximity Visualisation of Abstract Data. </span>
                                    PhD thesis, University of Cambridge Computer Laboratory (2000).</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="BBB*93">BBB*93</a>]</td>
                                    <td>Brodlie K., Brankin L., Banecki G., Gay A., Poon A., Wright H.: Grasparc&ndash;A Problem Solving Environment 
                                    Integrating Computation and Visualization. <span class="it">Proceedings of IEEE Visualization '93, IEEE Computer Society Press </span>
                                    (1993), 102&ndash;109.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="BC87">BC87</a>]</td>
                                    <td>Becker R., Cleveland W.: Brushing Scatterplots. <span class="it">Technometrics 29</span>, 2 (1987), 127&ndash;142.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="BH94">BH94</a>]</td>
                                    <td>Bederson B. B., Hollan J. D.: Pad++: A Zooming Graphical Interface for Exploring Alternate Interface Physics.
                                     <span class="it">Proceedings of UIST'94, ACM Symposium on User Interface Software and Technology </span>(1994), 17&ndash;26.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="Cha93">Cha93</a>]</td>
                                    <td>Chalmers M.: Using a Landscape Metaphor to Represent a Corpus of Documents. 
                                    <span class="it">Proceedings of COSIT '93,European Conference on Spatial Information Theory </span>
                                    (1993), 377&ndash;390.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="Cha96">Cha96</a>]</td>
                                    <td>Chalmers M.: A Linear Iteration Time Layout Algorithm for Visualising High-Dimensional Data. 
                                    <span class="it">Proceedings of IEEE Visualization </span>(1996), 127&ndash;132.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="CM97">CM97</a>]</td>
                                    <td>Card S. K., Mackinlay J. D.: The Structure of the Information Visualization Design Space. 
                                    <span class="it">Proceedings of InfoVis'97, IEEE Symposium on Information Visualization  </span>(1997), 92&ndash;99.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="CMS99">CMS99</a>]</td>
                                    <td>Card S. K., Mackinlay J. D., Shneiderman B.: 
                                    <span class="it">Information Visualisation &ndash; Using Vision to Think.</span> Morgan Kaufmann, 1999.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="CRM91">CRM91</a>]</td>
                                    <td>Card S. K., Robertson G. G., Mackinlay J. D.: The Information Visualizer, an Information Workspace. 
                                    <span class="it">Proceedings of CHI `91, ACM Human Factors in Computing Systems Conference </span>(1991), 181&ndash;188.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="DBM89">DBM89</a>]</td>
                                    <td>DeFanti T. A., Brown M. D., McCormick B. H.: Visualization: Expanding Scientific and Engineering Research Opportunities. 
                                    <span class="it">IEEE Computer 22</span>, 8 (1989), 12&ndash;25.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="Ead84">Ead84</a>]</td>
                                    <td>Eades P.: A Heuristic for Graph Drawing. <span class="it">Congressus Numerantium 42</span> (1984), 149&ndash;160.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="Eic94a">Eic94a</a>]</td>
                                    <td>Eick S.: Graphically Displaying Text. 
                                    <span class="it">Journal of Computational and Graphical Statistics 3</span>, 2 (1994). 127&ndash;142.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="Eic94b">Eic94b</a>]</td>
                                    <td> Eick S. :Data Visualization Sliders. 
                                    <span class="it">Proceedings of UIST '94, ACM Symposium on User Interface Software and Technology</span>,  ( November 1994), 119&ndash;120.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="FS95">FS95</a>]</td>
                                    <td>Fishkin K., Stone M. C.: Enhanced Dynamic Queries via Movable Filters. 
                                    <span class="it">Proceedings of ACM Conference on Human Factors in Computing Systems </span>(May 1995), 415&ndash;420.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="Fur86">Fur86</a>]</td>
                                    <td>Furnas G. W.: Generalized Fisheye Views. 
                                    <span class="it">Proceedings of the ACM Conference on Human Factors in Computing Systems </span>(1986), 16&ndash;23.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="Hae88">Hae88</a>]</td>
                                    <td>Haeberli P.: ConMan: A Visual Programming Language for Interactive Graphics. 
                                    <span class="it">Computer Graphics 22</span>, 4 (1988), 103&ndash;111.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="HK97">HK97</a>]</td>
                                    <td>Hearst M., Karadi C.: Cat-a-Cone: An Interactive Interface for Specifying Searches and Viewing Retrieval Results using a Large Category Hierarchy. 
                                    <span class="it">Proceedings of 20th Annual International ACM/SIGIR Conference </span>(July 1997), 246&ndash;255.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="Hon04">Hon04</a>]</td>
                                    <td>Honeycomb. http://www.hivegroup.com (2004).</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="JS91">JS91</a>]</td>
                                    <td>Johnson B., Shneiderman B.: Tree-Maps: A Space-Filling Approach to the Visualization of Hierarchical Information Structures. 
                                    <span class="it">Proceedings of IEEE Visualization '91 Conference </span>(1991), 284&ndash;291.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="KKL*00">KKL*00</a>]</td>
                                    <td>Kohonen T., Kaski S., Lagus K., Salojrvi J., Paatero V., Saarela A.: Self Organization of a Massive Document Collection. 
                                    <span class="it">IEEE Transactions on Neural Networks 11</span>, 3 (2000), 574&ndash;585.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="LRP95">LRP95</a>]</td>
                                    <td>Lamping J., Rao R., Pirolli P.: A Focus+Context Technique Based Upon Hyperbolic Geometry for Visualizing Large Hierarchies. 
                                    <span class="it">Proceedings of CHI'95,  ACM Conference on Human Factors in Computing Systems </span>(1995), 401&ndash;408.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="LSM91">LSM91</a>]</td>
                                    <td>Lin X., Soergel, D., Marchionini, G : A Self-Organizing Semantic Map for Information Retrieval.
                                    <span class="it">Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval </span>(1991), 262&ndash;269.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="MDB87">MDB87</a>]</td>
                                    <td>McCormick B. H., DeFanti T. A., Brown M. D., eds.: Visualization in Scientific Computing. 
                                    <span class="it">Computer Graphics 21</span>, 6 (November 1987).</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="MRC91">MRC91</a>]</td>
                                    <td>Mackinlay J. D., Robertson G. G., Card S. K.: The Perspective Wall: Detail and Context Smoothly Integrated. 
                                    <span class="it">Proceedings of CHI'91, ACM Conference on Human Factors in Computing Systems </span>(1991), 173&ndash;179.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="Nic04">Nic04</a>]</td>
                                    <td>Nickleby HFE Ltd. NicklebyKIT. http://www.nickleby.com (2004).</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="Nor01">Nor01</a>]</td>
                                    <td>North C.: Multiple Views and Tight Coupling in Visualization: A Language, Taxonomy, and System.  
                                    <span class="it">Proc. CSREA CISST 2001 Workshop of Fundamental Issues in Visualization </span>(2001), 626&ndash;632.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="Nor88">Nor88</a>]</td>
                                    <td>Norman D. A.: <span class="it">The Psychology of Everyday Things. </span>Basic Books, 1988.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="NS00a">NS00a</a>]</td>
                                    <td>North C., Shneiderman B.: Snap-Together Visualization: Can Users Construct and Operate Coordinated Visualizations? 
                                    <span class="it">International Journal of Human-Computer Studies 53</span>, (2000), 715&ndash;739.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="NS00b">NS00b</a>]</td>
                                    <td>North C., Shneiderman B.: Snap-Together Visualization: A User Interface for Coordinating Visualizations Via Relational Schemata. 
                                    <span class="it">Proceedings of Advanced Visual Interfaces </span>(2000), 128&ndash;135.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="NS01">NS01</a>]</td>
                                    <td>North C., Shneiderman B.: Component-Based, User-Constructed, Multiple-View Visualization. 
                                    <span class="it">Proc. ACM CHI 2001 </span>(2001), 201&ndash;202.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="NSP96">NSP96</a>]</td>
                                    <td>C. North. B. Shneiderman and C. Plaisant. User Controlled Overviews of an Image Library: A Case Study of the Visible Human. 
                                    <span class="it">Proceedings of DL'96, ACM Conference on Digital Libraries. </span>74&ndash;82. 1996.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="RBSW01">RBSW01</a>]</td>
                                    <td>Rodden K., Basalaj W., Sinclair D., Wood K.: Does Organisation by Similarity Assist Image Browsing? 
                                    <span class="it">Proceedings of the SIGCHI on Human Factors in Computing Systems </span>(2001), 190&ndash;197.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="RCK*97">RCK*97</a>]</td>
                                    <td>Roth S. F., Chuah M. C., Kerpedjiev S., Kolojejchick J., Lucas P.: Towards an Information Visualization Workspace: Combining Multiple Means of Expression. 
                                    <span class="it">Human-Computer Interaction Journal 12</span>, 1 (1997), 131&ndash;185.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="RCM89">RCM89</a>]</td>
                                    <td>Robertson G. G., Card S. K., Mackinlay J. D.: The Cognitive Coprocessor Architecture for Interactive User Interfaces. 
                                    <span class="it">Proceedings of the ACM SIGGRAPH Symposium on User Interface Software and Technology </span>(1989), 10&ndash;18.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="Ren94">Ren94</a>]</td>
                                    <td>Rennison E.: Galaxy of News: An Approach to Visualizing and Understanding Expansive News Landscapes. 
                                    <span class="it">Proceedings of UIST'94, ACM Symposium on User Interface Software and Technology </span>(1994), 3&ndash;12.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="RMC91">RMC91</a>]</td>
                                    <td>Robertson G. G., Mackinlay J. D. Card S. K.: Cone Trees: Animated 3D Visualizations of Hierarchical Information. 
                                    <span class="it">Proceedings of CHI'91, ACM Conference on Human Factors in Computing Systems </span>(1991), 189&ndash;194.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="Rob99">Rob99</a>]</td>
                                    <td>Roberts J. C.: Display Models for Visualization. 
                                    <span class="it">Proceedings of the International Conference on Information Visualization </span>(July 1999), 200&ndash;206.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="Rom01">Rom01</a>]</td>
                                    <td>Rome E.: Simulating Perceptual Clustering by Gestalt Principles. 
                                    <span class="it">25th Workshop of the Austrian Association for Pattern Recognition ÖAGM / AAPR </span>(June 2001), 191&ndash;198.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="SA82">SA82</a>]</td>
                                    <td>Spence R., Apperley M. D.: Data Base Navigation: An Office Environment for the Professional. 
                                    <span class="it">Behaviour and Information Technology 1</span>, 1 (1982), 43&ndash;54.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="Shn83">Shn83</a>]</td>
                                    <td> Shneiderman B.: Direct Manipulation: A Step Beyond Programming Languages. 
                                    <span class="it">IEEE Computer 16</span>, 8 (1983), 57&ndash;68.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="Shn94">Shn94</a>]</td>
                                    <td>Shneiderman B.: Dynamic Queries for Visual Information Seeking. 
                                    <span class="it">IEEE Software 11</span>, 6 (1994), 70&ndash;77.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="Shn96">Shn96</a>]</td>
                                    <td>Shneiderman B.: The Eyes Have it: A Task by Data Type Taxonomy for Information Visualization. 
                                    <span class="it">Proceedings of IEEE Workshop on Visual Languages </span>(1996), 336&ndash;343.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="Spo93">Spo93</a>]</td>
                                    <td>Spoerri A.: InfoCrystal: A Visual Tool for Information Retrieval and Management. 
                                    <span class="it">Proceedings of CIKM Conference </span>(1993), 11&ndash;20.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="TSDS96">TSDS96</a>]</td>
                                    <td>Tweedie L., Spence R., Dawkes H., Su H.: Externalising Abstract Mathematical Models. 
                                    <span class="it">Proceedings of ACM Conference on Human Factors in Computing Systems </span>(1996), 406&ndash;412.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="UFK*89">UFK*89</a>]</td>
                                    <td>Upson. C., Faulhaber  T. Jr., Kamens D., Laidlaw D., Schlegel D., Vroom J., Gurwitz R., van Dam A.: 
                                    The Application Visualization System: A Computational Environment for Scientific Visualization. 
                                    <span class="it">IEEE Computer Graphics and Applications </span>(July 1989), 30&ndash;42.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="WAM01">WAM01</a>]</td>
                                    <td>Weber M., Alexa M., Mueller W.: Visualizing Time Series on Spirals. 
                                    <span class="it">Proceedings of InfoVis'01, IEEE Symposium on Information Visualization </span>(2001), 7.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="Wis99">Wis99</a>]</td>
                                    <td>Wise J. A.: The Ecological Approach to Text Visualization. 
                                    <span class="it">Journal of the American Society for Information Science, Special Issue on Integrating 
                                    Multiple Overlapping Metadata Standards 50</span>, 13 (November 1999), 1224&ndash;1233.</td>
                                </tr>
                                <tr>
                                    <td valign="top">[<a name="WTP*95">WTP*95</a>]</td>
                                    <td>Wise J., Thomas J., Pennock K., Lantrip D., Pottier M., Schur A., Crow V.: Visualizing the Non-Visual: Spatial Analysis and Interaction with Information from Text Documents. 
                                    <span class="it">Proceedings of InfoVis'95, IEEE Symposium on Information Visualization </span>(1995). 51&ndash;58.</td>
                                </tr>
                            </tbody>
                        </table>
                    </div>
                    
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